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https://github.com/alessandroryo/rock-paper-scissors-classification

A deep learning project using Convolutional Neural Networks (CNNs) built with TensorFlow to classify hand gestures in the game of Rock-Paper-Scissors. This project showcases image processing techniques, data augmentation, and the power of CNNs for visual recognition tasks.
https://github.com/alessandroryo/rock-paper-scissors-classification

computer-vision convolutional-neural-networks data-augmentation deep-learning image-classification machine-learning python rock-paper-scissors tensorflow

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A deep learning project using Convolutional Neural Networks (CNNs) built with TensorFlow to classify hand gestures in the game of Rock-Paper-Scissors. This project showcases image processing techniques, data augmentation, and the power of CNNs for visual recognition tasks.

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README 

        
# Rock-Paper-Scissors Classification



This repository contains a machine learning project designed to classify images of hand gestures into one of three categories: rock, paper, or scissors. The project demonstrates the use of convolutional neural networks (CNNs) for image classification tasks, utilizing Python and TensorFlow.



## Table of Contents



- [Rock-Paper-Scissors Classification](#rock-paper-scissors-classification)

	- [Table of Contents](#table-of-contents)

	- [Project Overview](#project-overview)

	- [Dataset](#dataset)

	- [Model Architecture](#model-architecture)

	- [Training](#training)

	- [Results](#results)

	- [Contributing](#contributing)

	- [License](#license)

	- [Contact](#contact)



## Project Overview



The goal of this project is to build a model that can accurately classify images as either rock, paper, or scissors. The model is trained on a dataset of labeled images, and its performance is evaluated based on its accuracy in predicting the correct hand gesture. This project serves as a practical application of convolutional neural networks in the field of image recognition.



## Dataset



The dataset used in this project consists of images labeled as either "rock", "paper", or "scissors". The images are preprocessed to a consistent size and format before being fed into the neural network for training. The dataset includes a diverse set of hand gestures to ensure robust model performance.



- **Number of images**: Approximately 2,188 images.

- **Image format**: PNG, with each image resized to 300x200 pixels.



## Model Architecture



The model is built using a convolutional neural network (CNN) with the following architecture:



- **Input Layer**: Handles input images of size 300x200x3 (height x width x channels).

- **Convolutional Layers**: Multiple layers to capture spatial features from the images.

- **Pooling Layers**: Max-pooling layers to reduce the spatial dimensions and computational load.

- **Fully Connected Layers**: Dense layers for final classification.

- **Output Layer**: A softmax layer that outputs probabilities for the three classes: rock, paper, and scissors.



## Training



The model is trained using the following parameters:



- **Optimizer**: Adam optimizer

- **Loss Function**: Categorical cross-entropy

- **Metrics**: Accuracy

- **Epochs**: 20 (adjustable)

- **Batch Size**: 32 (adjustable)



The training process includes data augmentation to improve the model's generalization ability.



## Results



The model achieves an accuracy of over 95% on the test dataset, demonstrating its effectiveness in classifying hand gestures for the game of rock-paper-scissors.



## Contributing



Contributions are welcome! If you have any suggestions for improvements or find any bugs, please open an issue or submit a pull request.



## License



This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.



## Contact



For any questions or inquiries, please contact me via email:



- **Email**: